5.Assume that you are an employer and that yourmanaged care organization raises your rate based onthe results of data mining and predictive modelingsoftware. Would you accept the organization’ssoftware predictions?

Begins with a proposition by the user, whothen seeks to validate the truthfulness ofthe proposition

Discovery-driven data mining

Finds patterns, associations, andrelationships among the data in order touncover facts that were previouslyunknown or not even contemplated by anorganization

Data Mining Concepts

and Applications

–Marketing

–Banking

–Retailing and sales

–Manufacturing andproduction

–Brokerage andsecurities trading

–Insurance

–Computer hardwareand software

–Government anddefense

–Airlines

–Health care

–Broadcasting

–Police

–Homeland security

Data mining applications

Data Mining

Techniques and Tools

Data mining tools and techniques can beclassified based on the structure of thedata and the algorithms used:

Statistical methods

Decision trees

Defined as a root followed by internal nodes.Each node (including root) is labeled with aquestion and arcs associated with each nodecover all possible responses

Data Mining

Techniques and Tools

Data mining tools and techniques can beclassified based on the structure of thedata and the algorithms used:

Case-based reasoning

Neural computing

Intelligent agents

Genetic algorithms

Other tools

•Rule induction

•Data visualization

Data Mining

Techniques and Tools

A general algorithm for building a decisiontree:

1.Create a root node and select a splittingattribute.

2.Add a branch to the root node for each splitcandidate value and label

3.Take the following iterative steps:

a.Classify data by applying the split value.

b.If a stopping point is reached, then create leafnode and label it. Otherwise, build another subtree

Data Mining

Techniques and Tools

Data Mining

Techniques and Tools

Classes of data mining tools and techniquesas they relate to information and businessintelligence (BI) technologies

Mathematical and statistical analysis packages

Personalization tools for Web-based marketing

Analytics built into marketing platforms

Advanced CRM tools

Analytics added to other vertical industry-specificplatforms

Analytics added to database tools (e.g., OLAP)

Standalone data mining tools

Data Mining Project Processes

Data Mining Project Processes

Text Mining

Text mining

Application of data mining tononstructured or less structured text files.It entails the generation of meaningfulnumerical indices from the unstructuredtext and then processing these indicesusing various data mining algorithms